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. 2016 May 1;12(1):131-55.
doi: 10.1515/ijb-2015-0028.

A Case Study of the Impact of Data-Adaptive Versus Model-Based Estimation of the Propensity Scores on Causal Inferences from Three Inverse Probability Weighting Estimators

Romain Neugebauer, Julie A Schmittdiel, Mark J van der Laan

A Case Study of the Impact of Data-Adaptive Versus Model-Based Estimation of the Propensity Scores on Causal Inferences from Three Inverse Probability Weighting Estimators

Romain Neugebauer et al. Int J Biostat. .

Abstract

Objective: Consistent estimation of causal effects with inverse probability weighting estimators is known to rely on consistent estimation of propensity scores. To alleviate the bias expected from incorrect model specification for these nuisance parameters in observational studies, data-adaptive estimation and in particular an ensemble learning approach known as Super Learning has been proposed as an alternative to the common practice of estimation based on arbitrary model specification. While the theoretical arguments against the use of the latter haphazard estimation strategy are evident, the extent to which data-adaptive estimation can improve inferences in practice is not. Some practitioners may view bias concerns over arbitrary parametric assumptions as academic considerations that are inconsequential in practice. They may also be wary of data-adaptive estimation of the propensity scores for fear of greatly increasing estimation variability due to extreme weight values. With this report, we aim to contribute to the understanding of the potential practical consequences of the choice of estimation strategy for the propensity scores in real-world comparative effectiveness research.

Method: We implement secondary analyses of Electronic Health Record data from a large cohort of type 2 diabetes patients to evaluate the effects of four adaptive treatment intensification strategies for glucose control (dynamic treatment regimens) on subsequent development or progression of urinary albumin excretion. Three Inverse Probability Weighting estimators are implemented using both model-based and data-adaptive estimation strategies for the propensity scores. Their practical performances for proper confounding and selection bias adjustment are compared and evaluated against results from previous randomized experiments.

Conclusion: Results suggest both potential reduction in bias and increase in efficiency at the cost of an increase in computing time when using Super Learning to implement Inverse Probability Weighting estimators to draw causal inferences.

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Figures

Figure 1
Figure 1
Crude estimates over 16 quarters of the four survival curves associated with the four TI initiation strategies dθ with θ = 7, 7.5, 8, 8.5.
Figure 2
Figure 2
Unbounded IPW estimates (no weight truncation) over 16 quarters of the four survival curves associated with the four TI initiation strategies dθ with θ = 7, 7.5, 8, 8.5. The estimates on the top left, top right, bottom left, and bottom right plots were obtained based on the estimators gn, gn,t, gn,t,x, and gn,t,SL, respectively.
Figure 3
Figure 3
Bounded IPW estimates (no weight truncation) over 16 quarters of the four survival curves associated with the four TI initiation strategies dθ with θ = 7, 7.5, 8, 8.5. The estimates on the top left, top right, bottom left, and bottom right plots were obtained based on the estimators gn, gn,t, gn,t,x, and gn,t,SL, respectively.
Figure 4
Figure 4
Bounded hazard-based IPW estimates (no weight truncation) over 16 quarters of the four survival curves associated with the four TI initiation strategies dθ with θ = 7, 7.5, 8, 8.5. The estimates on the top left, top right, bottom left, and bottom right plots were obtained based on the estimators gn, gn,t, gn,t,x, and gn,t,SL, respectively.
Figure 5
Figure 5
Distribution of the IP weights assigned to failures observed under any of the four TI initiation strategies in the unbounded IPW estimation approach. Each plot represents the distribution of the IP weights derived with the estimators gn, gn,t, gn,t,x, and gn,t,SL in the interval indicated by the labels on the x axis. These intervals are defined by the 25th, 50th, 75th, 99th, and 99.9th percentiles of the IP weights derived with the estimator gn.
Figure 6
Figure 6
Distribution of the IP weights assigned to failures observed under any of the four TI initiation strategies in the bounded IPW estimation approach. Each plot represents the distribution of the IP weights derived with the estimators gn, gn,t, gn,t,x, and gn,t,SL in the interval indicated by the labels on the x axis. These intervals are defined by the 25th, 50th, 75th, 99th, and 99.9th percentiles of the IP weights derived with the estimator gn.
Figure 7
Figure 7
Distribution of the IP weights assigned to failures observed under any of the four TI initiation strategies in the hazard-based bounded IPW estimation approach. Each plot represents the distribution of the IP weights derived with the estimators gn, gn,t, gn,t,x, and gn,t,SL in the interval indicated by the labels on the x axis. These intervals are defined by the 25th, 50th, 75th, 99th, and 99.9th percentiles of the IP weights derived with the estimator gn.
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